Generative AI in Education: A Systematic Analysis of its Ethical Implications, Pedagogical and Evaluative Measures in Teaching Processes

Abstract

Generative artificial intelligence has managed to transform educational processes, generating new possibilities in teaching, learning, and assessment, but also important challenges at the academic level. This article presents a systematic review with the aim of analyzing its main implications in educational contexts. Therefore, six research questions were formulated aimed at the redesign of assessments, the implementation of institutional strategies, digital sustainability, the mitigation of algorithmic biases, AI literacy, and its use as an artificial evaluator. The methodological process was based on a structured search in academic databases, then a selection and quality evaluation of the studies. The results show that generative AI can strengthen feedback, personalization of learning, and pedagogical decision-making, as long as it is integrated in a responsible and transparent manner. However, risks associated with academic integrity, bias, and technological dependence are also identified. In conclusion, the incorporation of generative AI in education requires a critical approach, accompanied by institutional frameworks and digital literacy processes that allow its benefits to be harnessed without affecting the quality of learning.